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Date Submitted: May 18, 2020

(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Informatics management of tumor specimens in the era of Big Data: Challenges and solutions

Peifen Zhang;

XiaoHui Zheng;

XiZhao Li;

Lin Sun;

WeiHua Jia;

ABSTRACT

Biomedical data bears the potential to facilitate personalize diagnosis and precision treatment in the era of Big Data. Based on this, high-quality annotation of human specimens has become the primary mission of bio-bankers, especially for tumor bio-banks with large amounts of “omics” and clinical data. However, the lack of agreed-upon standardizations and the gap among heterogeneous databases make information application and communication a major challenge. International efforts are undergoing to develop national projects on informatics management. The aim of this paper is to provide references in data annotation and process to standardize and take full advantage of biomedical information. First, information categories that are vital for specimen applications, including sample attributes, external clinical and experimental data, are systematically listed to provide references for subsequent data mining. Second, commonly-used approaches in data collection, recording, extraction, transformation, integration and storage were summarized in support of data processes. In particular, a practical workflow of information annotation in daily bio-banking was drawn to help handling each step of the informatics management procedure. This review highlights the importance of informatics management of tumor specimens, presents the process of data standardization, and provides practical instructions for bio-bankers in specimen annotation and data management.

Citation

Please cite as:

Zhang P, Zheng X, Li X, Sun L, Jia W

Informatics management of tumor specimens in the era of Big Data: Challenges and solutions